It is common for sensed data sequences to have temporal patterns. For example, occupants of a building including sensors generate temporal patterns as they move from place to place. However, many automated systems used in buildings, e.g., elevator, heating, cooling, lighting, safety, and security systems, are largely insensitive to these patterns. Typically, these systems operate manually, in a simple pre-programmed day and night mode, or only respond to a current condition.
It is desired to determine temporal patterns in data sequences automatically.
Hidden Markov models (HMMs) have been used to represent patterns in data sequences, e.g., pedestrian flow Makris et al., “Automatic learning of an activity-based semantic scene model,” Proc. of IEEE Conference on Advanced Video and Signal Based Surveillance, July 2003, hand gestures Starner et al., “Real-time American sign language recognition from video using hidden Markov models,” Proceedings of International Symposium on Computer Vision, IEEE Computer Society Press 1995 and Wang et al., “Unsupervised analysis of human gestures,” IEEE Pacific Rim Conference on Multimedia, pp. 174-181, 2001, DNA sequences, Chudova et al, “Sequential pattern discovery under a Markov assumption,” Technical Report 02-08, Information and Computer Science Dept., University of California, Irvine, and human speech Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of IEEE, 77(2), pp. 257-285, 1989.
Hidden Markov models provide a powerful tool for discovering temporal patterns in human motion data, gestural and otherwise. However, most prior art modeling methods are computationally complex and time-consuming.